Schizophrenia is a complex and serious brain disorder.Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes(IDPs)to investigate the etiology of psychiat...Schizophrenia is a complex and serious brain disorder.Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes(IDPs)to investigate the etiology of psychiatric disorders.IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities.In this review,we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics.We first described IDPs through their phenotypic classification and neuroimaging genomics.Secondly,we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials.Thirdly,considering the genetic evidence of IDPs,we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization.Finally,we discussed machine learning as an optimum approach for validating biomarkers.Together,future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.展开更多
Schizophrenia(SZ),a complicated mental illness,shows up as incorrect beliefs,perceptual distortions,poor reasoning,and neurocognitive problems.Its varied character—varying in severity,development,and therapy response...Schizophrenia(SZ),a complicated mental illness,shows up as incorrect beliefs,perceptual distortions,poor reasoning,and neurocognitive problems.Its varied character—varying in severity,development,and therapy response—offers major diagnostic difficulty.While Magnetic Resonance Imaging(MRI)provides comprehensive structural and functional imaging for a better knowledge of the condition,Electroencephalography(EEG)with its great temporal resolution offers vital insights into neuronal malfunction in SZ.Deep learning(DL)techniques as well as advanced machine learning(ML)have been developed to improve SZ detection,therefore facilitating quick identification and improved patient recovery.This study presents SZAtt-Net,a DL framework designed for SZ detection and classification,integrating Convolutional Neural Networks(CNNs),Bidirectional Gated Recurrent Unit(BiGRU),along with Multilayer Perceptron(MLP)architectures.A key contribution of this work is the comprehensive ablation study of channel,self,spatial,and temporal attention mechanisms in DL,conducted to assess their impact on model performance across multimodal data.Notably,due to the absence of dedicated MRI datasets for SZ classification,this study repurposes an MRI segmentation dataset for classification,making it the first such attempt.As a unified model,SZAtt-Net is applied separately to three benchmark datasets—Kaggle EEG,LMSU EEG,and Hippocampus MRI—achieving accuracy rates of 99.37%and 98.92%using channel attention,and 96.33%using spatial attention,respectively.The proposed framework is also benchmarked against various pre-trained models,and a Gradient-weighted Class Activation Mapping(Grad-CAM)analysis is performed to enhance interpretability.This work underscores the clinical relevance of SZAtt-Net and outlines future research directions for improving accessible and accurate diagnostic solutions for SZ.展开更多
Circadian rhythms are considered a masterstroke of natural selection,which gradually increase the adaptability of species to the Earth’s rotation.Importantly,the nervous system plays a key role in allowing organisms ...Circadian rhythms are considered a masterstroke of natural selection,which gradually increase the adaptability of species to the Earth’s rotation.Importantly,the nervous system plays a key role in allowing organisms to maintain circadian rhythmicity.Circadian rhythms affect multiple aspects of cognitive functions(mainly via arousal),particularly those needed for effort-intensive cognitive tasks,which require considerable top-down executive control.These include inhibitory control,working memory,task switching,and psychomotor vigilance.This mini review highlights the recent advances in cognitive functioning in the optical and multimodal neuroimaging fields;it discusses the processing of brain cognitive functions during the circadian rhythm phase and the effects of the circadian rhythm on the cognitive component of the brain and the brain circuit supporting cognition.展开更多
基金Science Fund for Distinguished Young Scholars of Shaanxi Province(2021JC-02)Innovation Capability Support Program of Shaanxi Province(2022TD-44)+3 种基金Key Research and Development Project of Shaanxi Province(2022GXLH-01-22)National Natural Science Foundation of China(82101601)China Postdoctoral Science Foundation(2023T160517,2021M702612)Fundamental Research Funds for the Central Universities.
文摘Schizophrenia is a complex and serious brain disorder.Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes(IDPs)to investigate the etiology of psychiatric disorders.IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities.In this review,we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics.We first described IDPs through their phenotypic classification and neuroimaging genomics.Secondly,we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials.Thirdly,considering the genetic evidence of IDPs,we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization.Finally,we discussed machine learning as an optimum approach for validating biomarkers.Together,future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.
文摘Schizophrenia(SZ),a complicated mental illness,shows up as incorrect beliefs,perceptual distortions,poor reasoning,and neurocognitive problems.Its varied character—varying in severity,development,and therapy response—offers major diagnostic difficulty.While Magnetic Resonance Imaging(MRI)provides comprehensive structural and functional imaging for a better knowledge of the condition,Electroencephalography(EEG)with its great temporal resolution offers vital insights into neuronal malfunction in SZ.Deep learning(DL)techniques as well as advanced machine learning(ML)have been developed to improve SZ detection,therefore facilitating quick identification and improved patient recovery.This study presents SZAtt-Net,a DL framework designed for SZ detection and classification,integrating Convolutional Neural Networks(CNNs),Bidirectional Gated Recurrent Unit(BiGRU),along with Multilayer Perceptron(MLP)architectures.A key contribution of this work is the comprehensive ablation study of channel,self,spatial,and temporal attention mechanisms in DL,conducted to assess their impact on model performance across multimodal data.Notably,due to the absence of dedicated MRI datasets for SZ classification,this study repurposes an MRI segmentation dataset for classification,making it the first such attempt.As a unified model,SZAtt-Net is applied separately to three benchmark datasets—Kaggle EEG,LMSU EEG,and Hippocampus MRI—achieving accuracy rates of 99.37%and 98.92%using channel attention,and 96.33%using spatial attention,respectively.The proposed framework is also benchmarked against various pre-trained models,and a Gradient-weighted Class Activation Mapping(Grad-CAM)analysis is performed to enhance interpretability.This work underscores the clinical relevance of SZAtt-Net and outlines future research directions for improving accessible and accurate diagnostic solutions for SZ.
基金This study was supported by MYRG2019-00082-FHS and MYRG2018-00081-FHS Grants from the University of Macao,as well as FDCT 025/2015/A1 and FDCT 0011/2018/A1 Grants from the Macao Government.
文摘Circadian rhythms are considered a masterstroke of natural selection,which gradually increase the adaptability of species to the Earth’s rotation.Importantly,the nervous system plays a key role in allowing organisms to maintain circadian rhythmicity.Circadian rhythms affect multiple aspects of cognitive functions(mainly via arousal),particularly those needed for effort-intensive cognitive tasks,which require considerable top-down executive control.These include inhibitory control,working memory,task switching,and psychomotor vigilance.This mini review highlights the recent advances in cognitive functioning in the optical and multimodal neuroimaging fields;it discusses the processing of brain cognitive functions during the circadian rhythm phase and the effects of the circadian rhythm on the cognitive component of the brain and the brain circuit supporting cognition.
